ALBERT

Description: We examined the growth response of a brackish snail (Hydrobiidae) against multiple temperature treatments in a mesocosm located beside the Alfred Wegener Institute Wadden Sea Station on Sylt (55°01′19.2″N, 8°26′17.7″E). Bulk sediments were collected south of Pellworm (54° 31' 55.83"N, 8° 42' 40.36"E) at low tide on March 22, 2022, transferred to mesh-lined crates and introduced to mesocosm tanks (170 cm × 85 cm × 1800 L). Experimental warming treatments were conducted using three heaters per tank (Titanium heater 500 W, Aqua Medic, Bissendorf, Germany). The full specifications for the mesocosms are already published (Pansch et al., 2016). Throughout the experimental warming period, four sampling events (March 30, April 25, May 24, June 20) were conducted to core sediments. Sediment cores were washed and sieved (1mm mesh size) to disaggregate infauna. Individuals were separated for the common hydrobiid mudsnail, which were subsequently imaged in groups on a typical petri plate under stereomicroscopy. A semi-automatic object segmentation and size measurement approach was developed to rapidly differentiate and measure individuals from images. Segmentation was highly accurate and precise against manual length measurements (end-to-end; mm) collected in ImageJ for 4595 snails. Scaling the segmentation method across the full dataset estimated 〉40,000 snails and presented a complex species-specific response to warming. The enclosed dataset represents all raw, processed, and segmented images (n= 3201) produced by this study.

Description: 1. The expansion of scientific image data holds great promise to quantify individuals, size distributions and traits. Computer vision tools are especially powerful to automate data mining of images and thus have been applied widely across studies in aquatic and terrestrial ecology. Yet marine benthic communities, especially infauna, remain understudied despite their dominance of marine biomass, biodiversity and playing critical roles in ecosystem functioning. 2. Here, we disaggregated infauna from sediment cores taken throughout the spring transition (April-June) from a near-natural mesocosm setup under experimental warming (Ambient, +1.5 degrees C, +3.0 degrees C). Numerically abundant mudsnails were imaged in batches under stereomicroscopy, from which we automatically counted and sized individuals using a superpixel-based segmentation algorithm. Our segmentation approach was based on clustering superpixels, which naturally partition images by low-level properties (e.g., colour, shape and edges) and allow instance-based segmentation to extract all individuals from each image. 3. We demonstrate high accuracy and precision for counting and sizing individuals, through a procedure that is robust to the number of individuals per image (5-65) and to size ranges spanning an order of magnitude (〈750 mu m to 7.4 mm). The segmentation routine provided at least a fivefold increase in efficiency compared with manual measurements. Scaling this approach to a larger dataset tallied 〉40k individuals and revealed overall growth in response to springtime warming. 4. We illustrate that image processing and segmentation workflows can be built upon existing open-access R packages, underlining the potential for wider adoption of computer vision tools among ecologists. The image-based approach also generated reproducible data products that, alongside our scripts, we have made freely available. This work reinforces the need for next-generation monitoring of benthic communities, especially infauna, which can display differential responses to average warming.